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Vol. X & Issue No. 12 December - 2017

SALES FORECASTING STUDY IN AN AUTOMOBILE COMPANY- A CASE STUDY

Priyansha ChoukseyAparna DeshpandePraveer AgarwalDr. R.C. Gupta

AbstractSales forecasting is the most important activity of the Production planning and control and success of the firm is highly driven by accuracy of the forecasting. Sales forecasting aims to determine the demand of the product which helps in determining the available facilities and resources with the firm to meet the demand and procure more if necessary. In this research work, various critical success factors were identified and their overall and individual impact on sales forecasting was determined on the basis of real-time primary data and secondary data of the automobile firm situated at Pithampur specifically in the commercial vehicle segment study were done. In this paper, an attempt is made to apply regression analysis on external and internal factors to forecast sales of the company. This method was chosen as the best among others as it analyzes all driving factors of the sales unlike time-series forecasting technique. This study adds to the literature of sales forecasting, an approach that analyze and compare various macro and micro factor's sensitivity towards the forecasting accuracy.

Keywords: Sales Forecasting, Regression Model, Automobile industry, Internal and External factors.

1.� INTRODUCTIONForecasts play a key role in the management of operations

because they can provide rational guidelines and actions for

activities that must be managed in a competitive and

uncertain environment which makes forecasting an integral

part of decision making activities of management.

Forecasting the sales of products with high implied demand

uncertainty is difficult as compared to those with less

implied demand uncertainty. According to Diwakar and

Dalpati, there is a time lag between awareness of impending

events and occurrence of that event and this lead time is the

main reason for planning and forecasting. If the lead time is

zero or very small, there is no need for forecasting.

According to Lihua Yang and Baolin Li, strategies and

techniques to improve the sales forecasting accuracy has

become a hot spot for automotive industry, which can help

companies to improve the competitiveness of marketing.

According to Dieter and Yerzdi, the Indian automobile

industry needs to develop technologies and capabilities to

produce vehicles that meet future market needs, which

makes sales forecasting an important driving factor to fulfill

these needs. According to Rashmi Sharma and Ashok Sinha

the automotive sector is one of the core industries of the

Indian economy. Sales forecasting is crucial because without

a proper sales forecast a company cannot program to attain

the desired sales and marketing objectives. It is based on a

number of assumptions regarding customer and competitor

behavior as well as the market environment, and therefore,

its reliability depends upon a number of uncertain

parameters. Management analyzes previous sales

experience by product lines, territories, classes of customers,

and other relevant details. Management needs to consider a

time line long enough to detect trends and patterns in the

growth and the decline of sales volume.The purpose of the

research is to determine the factors that are responsible for the

accuracy of a sales forecast. The forecasting technique used

here is regression analysis, which helps compare the error

between the actual available sales forecast and the calculated

forecast.

2. �LITERATURE REVIEWAccording to Patricia.et.al, at the organizational level, sales

forecasting is very important to any retail business as its

outcome is used by many functions in the organization: sales

departments is able to get a good knowledge of the sales

volume of each product; purchasing department is able to

plan short- and long-term purchases; marketing department

is able to plan its actions and assess the impact of different

marketing strategies on sales volume; and finally logistics

department is able to define specific logistic needs. Accurate

forecasts of sales have the potential to increase the

profitability of retailers by improving the chain operations

efficiency and minimizing wastes. Sales forecasting also

considers the competitive position of the company with

respect to its market share; research and development;

quality of service, pricing and financing policies; and public

image. Forecasters also evaluate the quality and quantity of

the customer base to determine brand loyalty, response to

promotional efforts, economic viability, and credit

worthiness. Forecasters study the underlying assumptions of

trend variations to understand the important relationships in

determining the volume of sales. According to Bohanec

et.al, there are two main approaches in the literature to

analyze the evolution of vehicle sales. The first one is to use a

macro perspective and seek for the relationship between

aggregate sales and associated macroeconomic indicators.

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Vehicles are treated as homogenous products, whereas

product heterogeneity, brand differentiation, consumer

preferences or vehicle characteristics are not paying much

attention in this approach. The alternative approach has a

micro perspective, paying more attention to consumer

choices, vehicle properties as well as market structure and

hedonic pricing. Both approaches have advantages and

disadvantages.

According to Lim, the automobile industry is characterized

by a very volatile demand and impatient customers.

Furthermore, globalization has grown longer procurement

lead times of vehicle assembly plants which creates a

challenge for automotive manufacturers to cleverly adjust

production capacities with customer demands. These

demands require short delivery lead time and the possibility

to order customized vehicles as late as possible. However,

plants need to order parts several weeks beforehand from

distant suppliers, when the demand is not known yet. The

issue is to nd the best trade-off between these sales

requirements and industrial constraints while limiting stock

levels and emergency supplies due to parts shortages. As

stated in Prater et al., it is very difficult for a rm to ship parts

by sea to promptly react to changes in demands and to serve

individualized goods. Since the visibility on demands may

be lower than the procurement lead times, vehicle assembly

plants may order components based on unreliable forecasts.

Very few papers provide mathematical or quantitative

models, especially in a long procurement lead time situation

where forecasts are unreliable. Forecasts are not reliable

information about future demand, but they are the only

source of information to procure parts. Due to demand

uncertainty, a safety stock margin may be used. This margin

is dened by a percentage that allows to order more parts

than forecasts. For instance, with a safety stock margin of

10% the system will order 10% more parts than forecasted.

Emergency supplies are used in case of shortages. This

reflects the importance of choosing the best forecasting

method for the firm in order to reduce safety stock margin.

According to Yang and Baolin the forecasting methods can

be generally divided into two categories, namely, the

qualitative forecasting methods and quantitative forecasting

methods. The latter methods include some traditional

statistical methods, such as moving average, exponential

smoothing and multiple regression analysis. In fact, auto

sales are affected by many factors, such as the economic

situation, state policy, the income of the family, and so on.

These complicated factors cause the remarkable fluctuation

and non-linear characteristics of the historic sales data, so

some data don't have trends and display high fluctuation, to

solve the problem, some data mining algorithms are applied

to sales forecasting due to the complexity of sales data. J.

Scott Armstrong and Fred Collopy says that studies have

been conducted to identify which method will provide the

most accurate forecasts for a given class of time series.

Conclusions about the accuracy of various forecasting

methods typically require comparisons across many time

series. However, it is often difficult to obtain a large number

of series. Error measures also play an important role in

calibrating or refining a model so that it will forecast

accurately for a set of time series. According to Paul Dagum

et.al Forecasting models are dominated by uncertainty

because salient, observable variables define only a small

subset of relevant variables; unmodeled influences can lead

to unexpected consequences in a dynamic process. In the

Casual Forecasting method, forecaster constructs a

forecasting model that relates cost to the internal or

environmental variables believed to cause changes in the

observed cost. A model, attempting to unveil the structure

and operation of a process that determines our requirement

takes the form of one or more equation, usually statistical in

nature.

3. �PROBLEM STATEMENTTo determine the most influencing factor or group of factors

which affect the sales forecast to a great extent in automobile

industry based on case study. The research also dealt with

accuracy in prediction of sales forecast from the historical

data.

4. �METHODOLOGYThe main focus of this study is to

determine what could be a better method among various

forecasting methods, used to predict sales forecast for the

commercial vehicle segment, an automobile industry in

Pithampur, M.P. The thought behind, the selection of casual

forecasting techniques over time series was that the latter was

based totally on past historical data and trends observed,

whereas the former considered impact of various factors.

This paper encompasses the use of two casual models used in

forecasting. One being the Linear Regression model and the

other being the Multiple Regression model. Sales of year

2016 were considered as the dependent variable and various

relationships were established. The steps taken for the

determination of the critical factors affecting the sales

forecast is shown as flowchart in Figure 1, starting from data

collection of Sales forecast 2008-2016 from the company,

determining external and internal factors from literature,

applying multiple regression to both the external and internal

factors as a group, respectively, calculating the percentage

error for both the groups and comparing it, applying linear

regression model to the group with more percentage error and

then recording the results.

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Figure 1: Critical Success Factor Determination methodology

Various factors were enlisted from the literature review and brainstorming. These factors were classified as external factors and internal factors.

l External factors: These factors (macro factors) were the ones which affected the sales externally, i.e. Those factors which are beyond the control of the firm. These factors affect the entire industry and not just one single firm specifically. For example Excise Duty, Purchasing power parity etc.

l Internal Factors: These factors(micro factors) were the ones which affected the sales internally, i.e. Those factors within the firm and those which were under the control of the management and the company. These are factors which at times decide the leader in the market and the competitive edge of the firm. For example Cost of production and various types of other costs, technology used etc.

Yearly data related to all the individual factors were collected

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through various primary and few secondary sources like the annual reports of the firm, official government books etc. Then, Multiple Regressions was applied, the purpose behind this step was to determine the cumulative effect of all the factors blended together as one on the sales forecast. The regression analysis was done in Microsoft Excel. Multiple regressions was applied to all internal factors as one independent entity vs the total sales of 2016 being the dependent one. A similar procedure was followed for external factors. The purpose of this step was to determine the exclusive effects of both internal and external factors on the forecast and also as to how accurate and precise forecast, can be determined using the multiple regression. Then, error was calculated by comparing it with the sales data of 2016. Every company uses various forecasting tools to get their forecast for the coming period. At times it even considers a combination of various methods. Here, the study tried to get closer to the actual forecast data of 2016 by using the regression technique. The results, i.e. the percentage error calculated from both the internal and external factors separately was observed and compared. The percentage error resulting from multiple regressions of external factors was more than the regression result of the internal factors. Hence the requirement was felt to probe each external factor individually and determine which factor has major contribution in the fluctuations. Therefore the linear

regression model was applied on all the external factors as well as on the time factor and values were recorded. The thought behind this step was to determine, analyze and then decide the influencing factor of the sales forecasting, whether it could be controlled by the firm in any way or it has to be accepted as it is and then plan the strategy.

The proposed methodology is new and requires validation for further use. Under the scope of this study the external and internal factors identified, can prove to be a part of the subset of factors. The external factors are mostly governed by the government and are prone to changing over a period. A forecast for the future year consist of even several other elements like inventory level, safety stock, urgent orders etc. These particular factors are not considered under the scope of this study.

5. �RESULT This section presents the results of the research work in tabular form. Here table 1 depicts the comparative tabular representation of the actual sales forecast and predicted sales forecast followed forecasting error obtained by multiple regression and linear regression applied on various factors. Table 2 gives the date of the automobile company considered from year 2008 to 2016 and predicted sales forecast through this method in every year. The tables are displayed here in due sequence.

TABLE 1: Comparative Representation of the Actual and Predicted Sales Forecast

Particulars Forecast Actual

Sales

Forecast

Error in %

Sales Forecast

(Internal Factors)

51425.15 51690 0.512381505

Sales Forecast

(GDP)

55519.7 51690 -7.408976591

Sales Forecast

(PPP)

47729.16 51690 7.66268137

SF (No. Of

competitors)

3760585.714 51690 -7175.267391

S.F (Rate of

inflation)

49682.74 51690 3.883265622

S.F. (income per

capita)

48434.26 51690 6.298587735

S.F. (External

factors)

43703.72467 51690 15.45032952

Sales Forecast

(applied on all

factors)

49959.33317 51690 3.348165661

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Table 2: Data from 2008-2016 of the automobile firm in Pithampur.

YearGDP(US

D)

Purchasing

power Parity

(total,

national

currency unit

per US$)

Economic

Condition

No. of

Comp

etitors Rate of

Inflation

Income per

capita Cost of production(in crores)

Inventory( in Rs crores)

Cost of Machine

and Technology

Sales Revenue

Production( in million

units) Sales VOlume

Predicted

Sales

VOlume

2008 3786.75 12.929 16% 5 4.97 43426 1783.71 338.07 380.987 1822.48 22736 23744 23721.37

2009 4049.96 13.609 8% 5 5.88 43426 3044.07 218.96 375.75 3112.22 24013 24264 24110.18

2010 4404.56 14.653 8% 5 6.37 43426 4524.65 326.52 554.75 4701.63 38950 38202 38643.13

2011 4634.95 15.109 4% 5 8.87 51798 5826.9 427.9 856.71 6130.29 48630 48337 48086.1

2012 4832.82 16.001 5.00% 5 9.3 51798 6526.53 75.41 1496.17 6995.04 48054 48262 48244.67

2013 5089.58 16.725 26% 5 10.83 51798 1419.42 143.84 2119.73 41251 40654 40551 40465.64

2014 5391.69 16.997 30% 5 10.92 59770 2349.49 205.13 2738.68 40783 39707 39881 39969.92

2016 6000 17.24 24% 5 12.11 59770 4596.69 300.36 3322.93 63045 54103 51690

External FactorsInternal Factors

6. �CONCLUSION The proposed methodology is the easiest and simplest way to predict sales. This methodology used both multiple and linear regression which depicted changes in sales forecast due to individual independent variable changes and changes in sales forecast due to group of variable changes as whole. This methodology is based on cause and effect relationships among sales forecast and factors affecting it. This paper is based on primary data on past sales and secondary data on factors like sales volume, Inflation rate etc. The data proved helpful in identifying the reason behind the inaccuracy of sales forecast behind a wide range of factors, as well as, assessing the impacts of changes on existing norms, processes etc. It is associated with greater levels of internal validity due to systematic selection of factors. It has also been observed that overall factors consideration is important in sales prediction. Maximum accurate prediction is given by time variable which implies that Sales forecast variable depends mostly on the time variable. The results obtained by this regression were very close to the actual sales data. The result shows percentage error being less than one percent. Error in sales forecasting is mainly affected by external factors which are not under individual firm's control. There are many factors which are not under controlled that is why, any organization should make provision to accommodate changes when occur in external factors while predicting sales.

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AUTHORS

Priyansha Chouksey, Student of BE (IPE) Fourth year,Department of Industrial Production Engineering, SGSITS,INDORE 452003(M.P)Email: priyanshachouksey2@gmail.com

Aparna Deshpande, Student of BE (IPE) Fourth year, Department of Industrial Production Engineering, SGSITS, INDORE 452003(M.P)Email:deshpande.aparna995@gmail.com

Praveer Agarwal, PG Student, Department of Industrial Production Engineering, SGSITS, INDORE 452003(M.P)Email:praveerag@gmail.com

Dr. RC Gupta, Professor, Department of Industrial Production Engineering, SGSITS, INDORE 452003(M.P)Email:rcgupta.indore@gmail.com

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